Systems, methods and computer program products including features for coding and/or recovering data

ABSTRACT

Systems and methods are disclosed for processing data. In one exemplary implementation, there is provided a method of generating H output data streams from W data input streams produced from input data. Moreover, the method may include generating the H discrete output data streams via application of the W data inputs to one or more transforming components or processes having specified mathematic operations and/or a generator matrix functionality, wherein the W data inputs are recoverable via a recovery process capable of reproducing the W data inputs from a subset (any W members) of the H output data streams. Further exemplary implementations may comprise a transformation process that includes producing an H-sized intermediary for each of the W inputs, combining the H-sized intermediaries into an H-sized result, and processing the H-sized result into the H output data streams.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. provisional application No. 60/925,502, filed Apr. 19, 2007, which is incorporated herein by reference in entirety.

BACKGROUND

1. Field

The innovations herein relates to data processing, and, more particularly, to systems and methods consistent with creation of transformed data from plain text input data and/or other utilizations relating to such transformation.

2. Description of Related Information

Information including plain text data is commonly managed using a variety of processing techniques and tools, such as components that split the information into smaller portions for easier handling, transmission and storage. Existing systems for handling and subdividing information sometimes include components that provide the ability to reformulate sub-portions into the original data even when less than all of the sub-portions are immediately available at a node where the original data is desired. However, such systems generally facilitate only limited ability to recover data when loss occurs. They are unable to create or utilize additional streams or pieces of media containing redundant data to aid in future recovery.

Other existing systems may include components that manage information including plain text data. Such systems generally store, handle or transmit the original information in a manner where the plain text data is readily available to those with access to the system. A drawback of these systems is that they typically possess little or no means to protect underlying plain text from unauthorized or undesired viewing, and thus the plain text is often exposed to undesirable situations or individuals.

In sum, there is a need for systems and methods that may advantageously process plain text data by, for example, performing transformation and related processes that provide redundancy safeguards, enable protection of the original plain text data, or impart other advantages.

SUMMARY

Systems, methods, and articles of manufacture consistent with the invention are directed to transformation of input data including plain text into output data and/or other utilizations relating to transformation components/processes or the transformed data itself.

In one exemplary embodiment, there is provided a method of processing data from input data that includes plain text. Moreover, the method includes producing a first quantity of data inputs (W) from the input data, and generating a second quantity (H) of output data streams from the first quantity of data inputs via application of the data inputs to one or more transforming components that perform a transformation process using Galois field operations and a generator matrix characterized as being invertible in all subsets encounterable during later recovery of the input data. According to aspects of the innovations herein, the second quantity (H) of data output streams may be greater than (or greater than or equal to) the first quantity (W) of data inputs, and the transformation process may include, for each of the W inputs, producing an H-sized intermediary, combining all H-sized intermediaries into one H-sized result, and processing the one H-sized result into the H discrete output data streams. In one or more implementations, the W data inputs are recoverable via a recovery process capable of producing the W data inputs from any W members of the H output data streams.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as described. Further features and/or variations may be provided in addition to those set forth herein. For example, the innovations herein may be directed to various combinations and subcombinations of the disclosed features and/or combinations and subcombinations of several further features disclosed below in the detailed description.

DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which constitute a part of this specification, illustrate various embodiments and aspects of the innovations herein and, together with the description, explain the principles of the invention. In the drawings:

FIG. 1 is a block diagram of an exemplary system and/or method consistent with certain aspects related to the innovations herein;

FIGS. 2A-2B are block diagrams illustrating exemplary transformation components consistent with certain aspects related to the innovations herein;

FIGS. 3A-3C are diagrams illustrating exemplary transformation processing routines and processes consistent with certain aspects related to the innovations herein;

FIGS. 4A-4B are a logic/flow diagrams illustrating exemplary processes of transforming input data into H output data streams consistent with certain aspects related to the innovations herein;

FIGS. 5A-5B are logic/flow diagrams illustrating generator/recovery matrix-related functionality consistent with certain aspects related to the innovations herein;

FIGS. 6A-6B are logic/flow diagrams illustrating recovery matrix-related functionality consistent with certain aspects related to the innovations herein;

FIG. 7 is a logic/flow diagram illustrating additional generator/recovery matrix-related functionality consistent with certain aspects of the innovations herein;

FIG. 8 is diagram illustrating exemplary packet transmission functionality consistent with certain aspects related to the innovations herein;

FIG. 9 is a block diagram illustrating an exemplary implementation involving disk storage of the output data streams consistent with certain aspects related to the innovations herein;

FIG. 10 is a diagram illustrating exemplary data streams consistent with certain aspects related to the innovations herein;

FIG. 11 is a flow diagram illustrating an exemplary transformation process consistent with certain aspects of the innovations herein; and

FIG. 12 is block diagram illustrating exemplary hardware/logic components consistent with certain aspects related to the innovations herein.

DETAILED DESCRIPTION

Reference will now be made in detail to the invention, examples of which are illustrated in the accompanying drawings. The implementations set forth in the following description do not represent all implementations consistent with the claimed invention. Instead, they are merely some examples consistent with certain aspects related to the invention. Wherever possible, the same reference numbers will be used throughout the drawings to refer to the same or like parts.

Many systems and environments are used to store, package, and/or transmit information, such as information containing sensitive plain text data, via techniques that transform or subdivide original streams of information. Examples of such system and environments are disk arrays, backup systems, networking devices, add-in cards, applications programs, libraries, and/or shared object or operating system modules, in addition to many others. These systems and environments can be implemented using a variety of components, including via software components, hardware components, and/or hybrids thereof.

Systems, methods, articles of manufacture, components and/or subcomponents consistent with aspects of the innovations herein relate to creating one or more derived data streams from an input data stream such that the original data is no longer in the transformed data or data stream (in plain text) while having sufficient redundancy to recover the original data stream. Additionally, according to other aspects, original data or plain text may be maintained along with the transformed data while maintaining certain features of redundancy, recovery, and/or other innovations herein.

FIG. 1 illustrates a block diagram of an exemplary environment (system or method) 100 consistent with certain aspects related to the innovations herein. Referring to FIG. 1, environment 100 may comprise a transformation component/process 120, shown generally here, that receives input data 110, which may also include plain text data. Via any of the variety of circuitry, components or processes set forth below, transformation component/process 120 generates a quantity (H) of output data streams consistent with one or more of the features and/or advantages herein. According to certain further implementations, information in the H output data streams 132, 134, 136 may also be handled or utilized in a variety of additional implementations 160, such as in transmission over a network, storage, shipment, etc., in connection with achieving the innovations herein. When recovery of the original data or plain text is desired, a plurality of output data streams 150, which may be a set of streams 152, 154 less than H, are sent to a recovery component/process 160 to generate the desired output stream 170 (e.g., corresponding to the original input data 110). In other words, only W (W≦H) of these streams, files, etc. are required by the recovery component/process 160 to reconstruct the input stream or data file. Thus, H−W data files or streams might be lost/destroyed/corrupted yet the original data is still trivially recoverable. However, if fewer than W files are available, then none of the original input data is available/recoverable. Further, since the intermediate streams/files do not contain any of the plaintext of the input, not even small portions of the input can be recovered if fewer than W pieces are available.

According to certain aspects of the innovations herein, transformation component/process 120 uses finite field and/or Galois field operations (see, e.g., http://en.wikipedia.org/wiki/Finite_field arithmetic; http://en.wikipedia.org/wiki/Galois_field; James S. Plank, “A Tutorial on Reed-Solomon Coding for Fault-Tolerance in RAID-like Systems”, Software—Practice & Experience, 27(9), September, 1997; James S. Plank and Ying Ding, “Note: Correction to the 1997 Tutorial on Reed-Solomon Coding,” Software, Practice & Experience, Volume 35, Issue 2, February, 2005, pp. 189-194.) and a generator matrix to transform the input data 110 into the H output data streams 130. Further, as set forth throughout, the transformation component/process may create and use a variety of intermediary data elements in the process of generating the H output data streams.

While some exemplary disclosure herein is described in the context of all streams being created simultaneously, the streams may also be created individually or in groups. ‘Stream’ refers to an input file, output file or other source of data such as a buffer, socket, pipe, raw storage device or other source of digital data.

In the exemplary implementation illustrated in FIG. 2A, a system that processes data consistent with aspects of the innovations herein may comprise a processing component 201 that produces a first quantity (W) of intermediate streams or “data inputs” 203 from the input data, one or more transforming components 205 that perform a transformation process on the W data inputs using Galois field operations and a generator matrix characterized as being invertible in all subsets encounterable in an associated recovery process of the input data, and a generating component 207 that generates a second quantity (H) of output data streams from the W data inputs. (Details of the recovery process and associated recovery matrix are set forth below.) In one exemplary implementation, the transformation process may also include: for each of the W inputs, producing an H-sized intermediary; combining all H-sized intermediaries into one H-sized result; and processing the one H-sized result into the H output data streams, wherein none of the H output data streams corresponds to any W data input plain text. Other aspect may be implemented via the transformation process. For example, rows of the generator matrix may be rearranged to further scramble the input data, wherein information regarding the rearranged rows may then serve as a cryptography key.

The processing, transforming and generating components may be unitary, discrete, or distributed components. Additionally, the quantity H is greater than W, or it is greater than or equal to W, with certain distinct features and/or advantages applicable to both of these disparate aspects of the innovations herein. For example, while there is no ability to recover from a lost stream when H=W, aspects where these values are equal impart advantages consistent with data being smeared so as to obfuscate the original input data may be achieved. Further, the transformation process may include: for each of the W inputs, producing an H-sized intermediary; combining all H-sized intermediaries into one H-sized result: and processing the one H-sized result into the H output data streams. Due to the redundancy set forth herein, the W data inputs are recoverable via a recovery process capable of reproducing the W data inputs from any subset of the H output data streams with cardinality W.

In another exemplary implementation, a method of processing data consistent with aspects of the innovations herein may include producing a first quantity (W) of data inputs from the input data, and generating a second quantity (H) of output data streams from the first quantity of data inputs via application of the data inputs to one or more transforming components that perform a transformation process using Galois field operations and a generator matrix characterized as being invertible in all subsets encounterable during later recovery of the input data. According to aspects of the innovations herein, the quantity of H data output streams may be greater than (or greater than or equal to) the quantity W of data inputs, and the transformation process may include: for each of the W inputs, producing an H-sized intermediary; combining all H-sized intermediaries into one H-sized result; and processing the one H-sized result into the H discrete output data streams. In one or more implementations, the W data inputs are recoverable via a recovery process capable of producing the W data inputs from any W members of the H output data streams.

According to these features, the loss of one or more data streams is not a hindrance to the recovery of data, yet there is a threshold minimum number of streams required to recreate the original data. As such, aspects of the innovations herein may include advantages in certain situations. For example, where data is large enough to be spread across multiple pieces of media (by necessity or convenience), the ability to create additional pieces of media with additional redundant data to aid in future recovery can be a major benefit. The fact that the original content is not on any piece of media may also be advantageous. The fact that a threshold of pieces of media are required is also advantageous. Additionally, where there is a duty or desire to maintain confidentiality, the fact that the plaintext is not present may be advantageous. And the threshold number of pieces of media to recover is also advantageous. Finally, where there is risk of data loss due to corruption or media failures, certain advantages may be achieved, as set forth below.

Aspects consistent with the inventions and/or innovations herein may be implemented in a number of technologies, using a variety of components, such as one or more hardware components (see, inter alia, FIGS. 2A-2B, etc.), one or more software components (see, inter alia, FIGS. 3A-3C, etc.) or hybrids thereof, for example, via: disk arrays, backup systems (using almost any type of media imaginable), networking devices (which may split and/or join data streams as applicable or required), add-in cards, applications programs, libraries, and/or shared object or operating system modules, in addition to having many other applications. Aspects may use base 4, 8, 16, 32 or any other base for calculations. Optionally, it could include compression and/or encryption. Finally, the streams themselves may include error detecting and/or correcting redundancy to help cope with corruption and erasures.

FIG. 2B is a block diagram illustrating exemplary transformation components consistent with certain aspects related to the innovations herein. As shown in FIG. 2B, exemplary transforming components 200 include a memory 210, a memory interface component 220, calculation components or subcomponents 223, and a summing or combining component 230. According to certain implementations, memory 210 may be used to buffer various data streams. For example, FIG. 2B illustrates a first region 212 used to buffer the input stream, as well as a series of buffers 214, 216 used to buffer the first through H output streams. Management of the memory 210 may be achieved via a memory interface component 220, which performs task such as address generation, address translation, etc. Memory interface component 220 may also include a state management subcomponent 222 that handles the various pointers, counters and other state-related indicia and processes for buffer management. For example, insert pointers 204 and extract pointers 208 exist as state within state management subcomponent 222. Further, depending upon its ‘packaging’ logic, the state management subcomponent 222 may be configured to manage processing for transformation and recovery (also referred to as encoding and decoding), or for use in network environments (datagram processing/transmission), application level processing, or even for storage. Further, calculation components/subcomponents 223, such as calculation units 224, 226, may be implemented using RAM for lookup, PLA, discrete logic, or other similar technologies.

FIGS. 3A-3C are diagrams illustrating exemplary transformation processing routines and processes consistent with certain aspects related to the innovations herein. As shown in FIG. 3A, an exemplary transformation component 301 (e.g., software routine, etc.) may include an interface component that handles input data 313 and related data transfer and communication, a memory, component 307 that that controls memory access functionality, a management/processing component 309 for management and/or central processing operations, one or more output data interface components 311, as well as one or more transformation/processing components 303 which may, e.g., perform the primary computational functionality of the transformation component 301 or routine. According to one illustrative implementation, for example, transformation/processing component 303 may include: an initialize Galois operations 321 (gal_test) subcomponent or routine that performs Galois testing operations such as initializing the Galois tables, building up associated multiplication tables and verifying their correctness, implementing log tables, and performing multiplications and divisions thereof (though not strictly required, are a useful performance enhancement likely to be used in many implementations); generator matrix setup operations 323 (initialize_A) subcomponent that builds the relevant generator matrix such as a Vandermonde matrix, and initializes the generator matrix as well as inputs thereto; one or more recovery matrix setup 325 (fail_rows, invert_B, fail_data) subcomponents that performs various recovery or decoding setup processes such as randomly selecting rows to be used in the recovery matrix, and copying the rows thereto, as well as creating the recovery matrix itself; a generate output operations 327 (multiply_ADE) subcomponent that takes the input matrix and multiplies it by the generator matrix to obtain the final (E) matrix output yielding the H output data streams; a perform recovery operations 329 (multiply_BFQ) subcomponent that multiplies the transformed data by the recovery matrix to produce the original input data and/or plain text; and one or more check and finish operations 331 (check_result, initialize_D) including, for example, processes to verify that the results are consistent with the input data as well as processes to confirm that all of the row and column operations are finished so as to verify completion. See, for example, Appendix A, submitted herewith, as well as all of the Appendix materials submitted herewith, which are incorporated by reference in their entirety. Such subcomponents 321, 323, 325, 327, 329, 331 are shown for purpose of illustration not limitation, and may be otherwise organized, combined and/or distributed as with like routines to achieve the innovations herein.

Referring to FIG. 3B, an exemplary transformation processing, or encoding, procedure consistent with certain aspects related to the innovations herein is shown. FIG. 3B is a diagram illustrating one exemplary process 302 of encoding an input stream by first initializing data structures 308, then taking the input stream and splitting it into groups of length W, then performing matrix calculation (e.g., multiplying it by the generator matrix) to yield an output vector of length H, where each member of the output is then output to a different output stream 348. According to the exemplary process of FIG. 3B, data structures are first initialized 308. Then, for each input stream or file, exemplary process 302 executes a loop 312, 320, 324, 328 until the end of the stream or file is reached. The loop may include reading a buffer in 312, checking for end of file 316, and, if it's not the end of the file, processing the buffer W bytes at a time and performing a calculate and output subroutine for each portion processed. At the completion of each calculate and output subroutine, the procedure loops to the read step 312 to read in the next buffer portion 312. Once the end of the particular stream or file is reached 320, output buffer is padded until count percentage W drops to zero 328. At this time, a second calculate and output 330 subroutine may be performed to generate the H output data streams. In one exemplary implementation 340 of the second calculate and output subroutine, matrix calculations are performed 344 on the intermediary information and the resulting portions of the transformed information (1 byte, in this example) are stored in each of the H output buffers 348.

FIG. 3C shows steps associated with an exemplary recovery, or decoding, procedure complementary to the encoding procedure of FIG. 3B. In FIG. 3C, given W input streams (from a set of all H streams), the recovery matrix is created 356 by copying the lines from the generator matrix which correspond to the surviving W streams. This subroutine 356 may also include selecting generator matrix rows, inverting the matrix, and calculating preparations required for the ‘calculate and output’ subroutine. Next, this matrix is inverted using finite field and/or Galois arithmetic operations. Then, each of the W streams is read in turn 360 (in order of their lines in the recovery matrix) via a loop 360, 370, 372, 374 similar to FIG. 3B until an end of file or stream is reached 368 and a W width vector is created. The loop may include a calculate and output subroutine 372, 390 comprised of performing matrix calculations 394 and storing W bytes in the output buffer 398. Next, the W width vector is multiplied by the recovery matrix using Galois arithmetic and then processed 378 (e.g., truncated, etc.) to the proper size and length. The ensuing W width results correspond to the next W entries of the original output. (It should be noted that the original input may also be padded with up to W−1 extra entries via this process.)

FIGS. 4A-4B are a logic/flow diagrams illustrating exemplary processes of transforming and recovering input data to and from H output data streams consistent with certain aspects related to the innovations herein. The transformation process 410 of FIG. 4A shows crossing (multiplying) portions 420 of the input stream 110 with the generator matrix 430 to produce intermediary results 440 of width H, which are then processed into the H output data streams 130. The recovery process 450 of FIG. 4B shows crossing (multiplication) of the recovery matrix 470 with portions 460 of the W output streams 150 used to decode. Again, intermediary results 480 of width W are obtained, which are then processed into the original input stream 110. In exemplary encoding and decoding processes such as these, the matrix operations may be performed according to finite field rules for suitable generator polynomials of given base (such as base 8, etc.).

FIGS. 5A-5B are logic/flow diagrams illustrating generator/recovery matrix functionality consistent with certain aspects related to the innovations herein. Referring to FIG. 5A, certain matrix-based functionality, which may be implemented in pure software, is shown. FIG. 5A shows tables 540, which may be calculated by the transformation component, for lookup of multiplication results based on the generator/recovery matrix. Here, for example, each byte lane would contain a table 540 having a width 512 equal to the register width and a height 514 commensurate with the base value (e.g., 256 entries in height for base 8 calculations), where the table is used to look up multiplication by constant from the matrix. Calculation of such tables 540 enables evaluation of an entire column of a generator or recovery matrix by table lookup. Further, by merging multiplication by constant tables, 4 or more rows may be calculated at once. To produce an output 536, desired rows 522, 524, 526, etc. may be joined via a finite field or Galois add operation such as XOR (exclusive OR) to yield a sum 532 providing the output 536.

Referring to FIG. 5B, certain matrix-based functionality, which may be implemented in software plus special purpose (SIMD) instructions, is shown. For purposes of illustration, the example of FIG. 5B assumes SSE3 instructions, though the innovations herein are not limited to any one type of such instructions. Similar to FIG. 5A, FIG. 5B shows tables 540, which may be calculated by the transformation component, for lookup of multiplication results based on the generator/recovery matrix. Here, for example, each byte lane would contain a table 540 having a width 542 of 16 bytes (i.e., SSE3 register width) and a height 544 commensurate with the base value (e.g., 256 entries in height for base 8 calculations), where the table is used to look up multiplication by constant from the matrix. Calculation of such tables 540 enables evaluation of an entire column of a generator or recovery matrix by table lookup. Further, by merging multiplication by constant tables, 4 or more rows may be calculated at once. To produce an output 566, desired rows 552, 554, 556, etc. may be joined via a finite field or Galois add operation such as XOR (exclusive OR) to yield a sum 562 providing the output 566.

Referring to FIG. 6A, certain matrix-based functionality, which may be implemented in hardware, is shown. FIG. 6A shows exemplary encoding or decoding systems including various components or devices that may implement one or more of the transformation processes set forth herein. Here, for example, a register 620 of size W is used to hold the input stream for processing with the encoding/decoding table values. According to certain implementations, the tables values would be constant for a given W and H for encoding, while, for decoding, the table values would vary depending upon W, H, and which streams were used for recovery. In this example, the lookup tables 630 may be stored in one or more memory devices initialized to width H*2**base and height, or 2**base, i.e., in common cases, 128 bits wide by 256 entries. As such, the values A, B, C, D from the register 620 are crossed (multiplied) with corresponding entries from the tables 630 to yield intermediary results 642, 644, 646, 646. These intermediary result, in turn, are summed 650 (XORed) to produce the final desired result/output 652.

FIG. 6B illustrates further matrix-based functionality, which may also be implemented in hardware. The implementation of FIG. 6B utilizes a register 620 and input stream 610 processing to determine input values 624 for mapping consistent with the example of FIG. 6A. The implementation of FIG. 6B, however, includes a series of arrays 662, 664, 666, 668, which may be logic arrays, PLAs, or the equivalent, and which translate the given input values 624 into the intermediary output results 672, 674, 676, 678. Lastly, the intermediary results are again summed (XORed) to produce the final output result 682 of the component in question.

FIG. 7 is a diagram illustrating additional generator/recovery matrix-related functionality, including integration of a random data component to provide encryption, consistent with certain aspects of the innovations herein. The logic/flow diagram 700 of FIG. 7, in general, includes input stream 710, portions 720 thereof, a generator or recovery matrix 732, intermediary results 740, and output 750 features consistent with those of FIGS. 4A-4B. Further, diagram 700 also illustrates an exemplary application of pseudo-random data 736 into the matrix operations to provide for encryption of the output stream 750. In the exemplary encryption scheme illustrated, the pseudo-random data 736 is processed into a vector component 734 for integration into the transformation process to produce the encrypted output 750. Other applications of a random or pseudo-random data 736 include application of pseudo-random data to the H-sized intermediaries or the H-sized result via an exclusive OR operation to provide encryption to the H output data streams.

Networking

The present innovations having provide particularized benefits in various networking systems and methods. First implementations in this regard relate to moving large files, such as when transferring files from facilities have several independent network connections. According to aspects of the innovations herein, multiple data transfers may be created (either concurrently or sequentially) which can be combined at the receiving end. The net result is shorter time to transfer the data than would normally be required by using a single TCP session over a single connection. Additionally, the resulting data would be more likely to be correct given the higher reliability of the recovery mechanism. Further, if more than one network connection is available, this technique allows all connections to be used concurrently for additional throughput.

Additional network implementations relate to new networking protocols. Existing protocols, such as TCP, suffer from a variety of well known drawbacks, and most alternatives to TCP are based upon the idea of sending data more quickly and using more resources at each end. In contrast, systems and methods of present disclosure may include innovations consistent with a new approach to data movement: the use of data correction in the face of dropped packets. According to some aspects of these innovations, an input stream may be divided into segments, for example, 128 datagrams. A sender may then use innovations herein to calculate 256 new datagrams (W=128, H=256). As illustrated in FIG. 8, the sender then begins sending the first 128 of the new datagrams. Once this segment 804 of the data stream 802 is transmitted, the sender begins to transmit the subsequent segment 804. When the receiver notices the transition from one segment to the next, it counts the valid datagrams in each segment. If a segment has the proper number of datagrams, then the data is recovered and the receiver acknowledges (either explicitly or implicitly) the correct transfer of the segment. If one or more datagrams are corrupted or missing 806, the receiver cannot reconstruct the input stream when it attempts recovery because of the lost or corrupted packet. Here, the receiver typically times out or sends information back to the sender that a packet is missing. The receiver may inform the sender either explicitly or implicitly. (Explicitly would involve the receiver informing the sender that it needs x additional datagrams. Implicitly could involve a timeout whereby the sender will automatically send an additional datagram after a period of time without an acknowledgement.) As such, sender sends an additional redundant packet which allows reconstruction of all original data from packets 0 through n−1. Eventually, the sender will send enough datagrams to recreate the entire segment. If necessary, the sender can wrap around and resend packets or the sender can generate additional datagrams.

Systems, methods and components consistent with this approach enable the sender to push the network's capacity to the limit such that any additional load will result in substantial packet loss. (The overhead of sending one additional datagram out of say 128 is tiny, yet it makes it possible for the sender to operate extremely close to capacity.) These systems, methods and components may include one or more advantages wherein, for example: (1) the plain text of the messages is never transmitted, (2) the loss of an arbitrary number of packets (up to H−W) will not result in lost of data, (3) the amount of traffic from the receiver to the sender is very small (i.e., much less than TCP), (4) more effective operation where data loss rates are zero, very low or very high (>50%), and/or (5) the transmission of one group of W packets may overlap with the correction of previous groups of packets (therefore allowing the sender to continuously send and the receiver to continuously receive without the need for a pause and a round-trip delay).

Disk Controller or Driver Components

According to one or more further aspects of the innovations herein, data store or disk arrays are provided in which no plaintext is stored yet the array can survive the destruction of an arbitrary number of disks or devices. In this regard, FIG. 9 is a block diagram illustrating an exemplary implementation involving disk storage of the output data streams consistent with certain aspects related to the innovations herein. System 900 of FIG. 9 may includes a main processing component 901 that receives input data 913 via a data interface 905, such as data from over a host bus 917. The main processing component 901 may also include a memory buffer 907, a management/processing component 909 for management and/or central processing operations, one or more output data interface components 911, as well as one or more transformation/processing components 903 which may, e.g., perform the primary computational functionality of the main processing component 901. According to one exemplary implementation, for example, the transformation/processing component 903 may perform the finite filed/Galois operations and matrix manipulation features and functionality set forth herein. Further, then, data transformed by component 903 may be sent to one or more data interfaces 911, which provide H output data streams to H disks 917, storage devices, etc.

Another variant, here, is an implementation using both disks and remote storage devices (either over a SAN, LAN or the internet for example). In the extreme case, several storage devices could be placed around the internet (perhaps in different countries for various legal or logistical reasons). In one more general implementation, H output data streams may be stored in disparate data stores, wherein no single data store contains output streams sufficient to recover the input data, such that the destruction of or loss of connectivity to or otherwise unavailability any quantity of the data stores from 1 through H−W does not impede the ability to recover the stored data. As such, a client is required to access a minimum subset of the disks and/or devices to recover the original/plain text data. Moreover, the transformed data may be regenerated by accessing at least W of the H output data streams, without revelation of the plain text. For reasons consistent with the regulations (privacy, trademark, etc.) discussed herein, especially those providing restriction in only one nation or region, the disparate data stores may be located in different and/or multiple jurisdictions, different and/or multiple countries, areas without established or clearly agreed upon jurisdiction or governmental control. Further, aspects of the innovations herein have particularized benefit with regard to storing data that is backup data, historical data and/or archival data. And similar benefits also apply to the transformation and storage of data covered by a heightened duty or privilege such as medical information, accounting information, attorney-client privileged information, email, personnel files, content declared classified, thereby requiring storage which is both safer from disaster than normal and also more difficult to be disclosed to third parties.

One advantage to the above is that the physical theft of devices would not result in loss of data or ability to recover sensitive data by a malicious party. Indeed, there are a number of substantive regulations around the globe, both existing and proposed, which cover protection of plaintext confidential data. This technology overcomes numerous drawbacks associated with existing systems and methods that fall short of addressing the technological issues behind these regulations.

According to alternate aspects, a kernel module, SAN device, iSCSI device or a physical disk controller may be implemented to embody the innovations, here. While the overhead would be substantial, it enables one to insert/remove disks at will, with certain limited restrictions.

Similarly, these aspects may also be implemented as a tape (or other storage medium) controller and/or driver. In the case of a backup controller, the output would be H pieces of media of which only W are required for recovery. And because backups are often faulty, typically due to error-laden media, aspects of the innovations herein overcome the drawbacks of existing backup technologies when H>>W is used.

In another exemplary aspect, the above controller is teamed with CD or DVD-style optical disks or other removable storage in a juke box. The resulting system has almost unlimited capacity yet has extremely high reliability.

In yet another exemplary aspect, the above technique is teamed with ‘Flash Disks’ or other storage tending to contain bad regions and/or to ‘wear out’ under certain usage patterns. By writing data in groups of H units (encoding W units of data), aspects of the innovations herein increase the lifetime and reliability of such a storage device at little or no operational cost, save the loss in capacity represented by the H−W additional units of storage.

Unix-Style ‘Filter’

A transforming or ‘encoding’ program with one input and H outputs generated using one or more aspects of this technology may also be utilized to implement a unix-style filter. Similarly, a recovery or ‘decoding’ program with one output and somewhere between W inputs using this technology may also be employed consistent with such filtration. Finally, a ‘paranoid’ transforming or encoding program, which uses more than W inputs (generating multiple models and cross checking), may also be employed to improve accuracy and/or reliability.

According to further implementations, as shown in part in FIG. 10, intermediate data streams may be augmented by breaking them into sections or “chapters” 1010 and inserting headers 1006, 1008 or other tags or data elements into the data stream. The headers may contain metadata such as length, time stamps, checksums (or other longitudinal error detection and/or correction information), and/or compression information. Inter alia, innovations consistent with these features enable a recovery program to ignore a corrupted chapter on one input stream without terminating the data stream. In very long data transfers, for example, it is possible that all data streams would have corruption so the ability to skip an erroneous chapter (sometime also referred to as a “segment,” herein) yet continue to use the data stream in the future dramatically increases the reliability of the system.

FIG. 10 is a diagram 1000 illustrating exemplary divisions of data streams 1002 consistent with certain aspects related to the innovations herein. According to aspects of the present innovations, systems and methods herein may further comprise dividing the H output data streams into chapters so as to produce sets of corresponding chapters of each stream such that a desired chapter of a set may be recovered from corresponding members/chapters of the set.

For example, as shown in FIG. 10, the first segment of stream #1 is a bad chapter 1004. Because of the innovations herein, a damaged chapter may be quickly isolated allowing it to be recovered or to be bypassed in the general recovery process without bypassing the entire stream. Therefore, since in this example W=4, there are 5 segments to choose from while only 4 are required. As a result, the system could use segments from streams 0, 2, 3, and 4 to recover the original data.

If desired, additional recovery models may be constructed involving stream 5 to provide an additional level of checking on the output. Here, there are 5 combinations of 4 ‘good’ streams out of 5 ‘good’ streams so it would be possible to build 4 additional check models. All models would be in conformance unless there is some input corruption. If they are not in conformance, aspects of the innovations herein enable determination of which contributing chapter is in error by elimination. In situations such as this, it is even possible to recover from both a checksum (or similar) error plus an error not caught by the checksum. It is also possible that the network receiver could ask the transmitter to send a redundant segment instead of an entire stream. If, for example, segment 7 had a CRC error, the receiver could ask for another segment 7 to be included in the data stream somehow, or else sent in a new data stream. Then this single redundant chapter is used to overcome the corrupted chapter. The transmitter need not even know which stream's segment was damaged. This affords several advantages, such as reducing the amount of information which must be transmitted.

According to still further aspects of the innovations herein, compression may also be implemented before the data is split. Similarly decompression could take place immediately after the data is regenerated in the recovery program. This becomes a tradeoff of CPU effort versus storage space or network bandwidth. The more CPU power available, the harder the compression algorithm(s) can be worked to increase the net throughput speed.

According to additional aspects of the innovations herein, data may be encrypted at various points along the transformation process. For example, as seen in FIG. 11, two advantageous points to encrypt are: (1) between the compression engine 1104 and the transformation component 1108, as shown by crypto engine 1106; and (2) after the data streams have been transformed, as shown by crypto engine 1112. FIG. 11 illustrates an exemplary flow diagram showing transmission of the input data stream 1102 through to the transformation component, where an encode/decode key 1110 may also be utilized. After passing through the optional crypto engine 1112, the transformed data is distributed 1114 to H output data streams 1116.

The data throughput could be enhanced via use of a hardware encoder and decoder. Indeed, aspects of the innovations herein relating to the finite field matrix arithmetic are particularly adapted to a hardware solution. As encoder's coefficients are constants, creation of a dedicated engine is thereby simplified and also affords higher performance. In general, the decoder's coefficients are not known until they are calculated, though they can be calculated in advance and stored until required.

Innovations Over Bit Torrent

Given an exemplary 1 GB data file (a movie or OS distribution or similar) to be distributed to thousands of clients via the internet, aspects of the innovations herein may be used create outputs based on relatively high H and W values (e.g., here, for the sake of illustration, W=32, H=64, though these could readily be larger). These exemplary values would result in each data file being about 32 megabytes, which can then be downloaded quickly given today's network speeds. As clients engage to download the data file, each client is provided a different stream as well as information instructing the clients to communicate with each other to recreate the original file. Innovations consistent with this approach may have one or more of a variety of advantages: (1) there is a high probability that one can recreate the original file with just W downloads; (2) there is an extremely high probability that one can recreate the original file with W+1 or W+2 downloads, even in the face of noisy transfers and questionable data quality; (3) the original server's bandwidth requirements are very low, as most of the communication comes between clients; and/or (4) all of the other advantages of Bit Torrent are maintained. Further, according to certain implementations, none of the streams are in violation of various regulations such as those concerning privacy, personal and/or financial informational, copyright violations, etc., since the streamed data does not contain the original content, nor is it sufficient to recreate the original content. Moreover, a majority of the processing nodes, such as distribution nodes or download sites may be configured such that they cannot possibly recover the original data on their own absent explicit provision of decoding information (recovery matrices) necessary to recover the plain text data. Indeed, such configurations may commonly be desired for reasons such as the security features and/or other innovations set forth herein.

The following example illustrates innovations consistent with the above. Given a 1 TB file to be moved from site A to sites B and C, where all of the sites have dedicated T1 lines (˜0.2 megabytes/second) between each other. If ftp(1) is used to transfer the files, it would take 1024/0.2 second or about 1.42 hours to get the file to B and C assuming no slowdowns or overhead. However, according to the innovations herein, the file may be split (into “shards”) using W=2, H=2 (or more), then you could send the first half of the stream to site B and the second half to site C along with instructions for sites B and C to share data. These instructions may include data enabling retransmission of the sub-streams to the recipients such that transmission bandwidth is increased. Here, then, it would take 0.71 hours to move the data. Moreover, as additional destination sites are added, using higher W and H values, transmission times are reduced at an increasingly higher rate. Indeed, the more concurrent downloads, the higher your effective bandwidth becomes.

With regard to technical descriptions of this functionality, systems and methods herein may comprise splitting the H output data streams into shards so as to produce sets of corresponding shards of each stream such that a desired shard of a set may be recovered from corresponding shard members of the set, and saving the shards to media storage components for storage or to enable transfer to another location, wherein only a subset of the media storage components is needed to recover plain text data as the input data may be recovered from a set of at least W members of each set of the corresponding shards. Further, saved shards may be configured for transmission via multiple paths such that transmission bandwidth is increased and/or recoverability of the shards is enabled. Here again, only a subset of the media storage components is needed to recover plain text data as the input data may be recovered from a set of at least W members of each set of the corresponding shards.

Additionally, if the connections are ‘lossy,’ then W and H may be increased to maintain high data transfer rates despite the losses. Increasing W breaks the output into smaller streams which are faster to send. Increasing H generates ‘extra’ streams which will make it easier to recover if a stream is corrupted. According to aspects of the innovations, here, incremental streams may simply be sent until the recipient site indicates that it has received enough streams to recover the original data.

Other aspects achieve innovations with regard to software distribution. Given a piece of software (or other big binary object), the contents can be split up and placed on a DVD or equivalent distribution media with W−1 pieces. As a result, end users can load the DVD onto their systems but not use the software (or data) until the final piece is provided (by media, internet, etc.). This provides a nice license validation mechanism and guarantees only users with access to the last piece (‘the license key’) would be able to use the software. For example, the one or more final shards required to complete the software or data item are only transmitted to a node that provides consideration and/or satisfies a condition. These innovations also extend to encryption features, as random data may be inserted into the shards such that recovery of plain text data further requires information regarding the inserted random data.

Aspects of the inventions, innovations and technologies herein may be also based upon coding theory which implements Reed Solomon Erasure Codes. Applications of such Codes for disk storage is set forth in U.S. patent application Ser. No. 10/845,546, published as US 2005/0257083A1, which is incorporated herein by reference in its entirety. In short, an array of H disks can be configured for W data disks and H−W redundant disks. Such an array can recover the written data so long as a total of W disks survive. (H<W) For example an array of 16 disks with 12 data disks. Any 12 disk subset of the 16 disks can be used to recover all 12 disks worth of data. The other 4 disks are called ‘redundant disks’ because they contain ‘redundant data’—data which is not the plaintext but is in fact derived from the plaintext.

Aspects herein may involve data on the data disks not being stored. In one exemplary implementation, an array of 16 disks with 4 data disks may be created, which gives 12 redundant disks. However, the 4 data disks may be disregarded or, in fact, never implemented. Here, then, no disk contains the original plaintext, yet every byte of data can be recovered. The mathematical term for this is that the system is no longer systematic. Indeed, in the above example, all that is required for recovery is any 4 of the 12 redundant disks. However, none of the existing disks contains the original data.

Now forget about disks and consider just data—either files, streams of data, backup tapes or other abstractions. In the above example, if the data was stored on 12 backup tapes or DVDs, then only 4 would need to survive. An administrator could send three disks to four different locations. Or even one disk or tape to each of 12 different locations. A thief would have to recover disks from more than one location before the backups could be read. Thus greater privacy along with higher reliability may be afforded by such systems, methods and components. Cryptologists refer to this as a “Shared Secret” problem.

In the case of a single stream of data, it is trivial to make the data ‘appear’ to be multiple streams. One trick is simply to pull off chunks of a data stream and number them 0 through n−1 as required. Doing this at the byte level is easy. This approach generates n input streams quickly and simply. The only downside to this approach is that the output must be padded and up to n−1 additional chunks of data could be output. The system must somehow know to remove the padding or ignore it somehow.

Further, by performing compression before processing, overhead of the redundancy can be reduced dramatically, while at the same time substantially improving the chances of recovering the compressed data in the future. Indeed, aspects herein overcome drawbacks in systems where data can usually only be recovered from the plaintext, not from a compressed file. Using the present innovations, the odds of having a corrupted compressed file drop dramatically. As such, the reliability of compression increases—at the cost of more bits. While compression may be ignored in some situations, in certain implementations it mitigates the growth in bits of output.

Another insertion:—when data is stored with an error check or correction code (ECC, CRC, etc.), then if we have a vector of input sectors ‘A’ and ‘B’ output sectors which follow the math herein described, then the mathematical relationship between the data in A and B is also reflected in the error check or correction codes for A and B if the error check or correction code form a group with the associated data. (There is a predecessor claim in one of my storage patents on this.)

Instead of disks, we next consider just data (i.e., files, streams of data, backup tapes or other abstractions). In the above example, if the data was stored on twelve backup tapes or DVDs, only four would need to survive. An administrator could send three disks to four different locations. Or even one disk or tape to each of 12 different locations. A thief would have to recover disks from more than one location before the backups could be read. Greater privacy, along with higher reliability, may thus be afforded by such systems, methods and components. Cryptologists refer to this as a “Shared Secret” problem.

In the case of a single stream of data, it is trivial to make the data ‘appear’ to be multiple streams. One such method involves simply pulling off chunks of a data stream and numbering them 0 through n−1, as required. At the byte level, this method is not complicated since n input streams are generated quickly and simply. A downside to this approach, however, is that the output must be padded and up to n−1 additional chunks of data could be output. Therefore, the system must be configured to either remove or ignore the padding.

Tying in compression before processing, one can normally reduce the overhead of the redundancy dramatically while at the same time substantially improving the chances of recovering the compressed data in the future. Many people, however, are reluctant to use data compression because of its notorious reputation for yielding corrupted compressed files or file systems. Moreover, the general belief is that data can usually be recovered from the plaintext, but can't be recovered from a compressed file. By utilizing the present invention, however, the odds of having a corrupted compressed file drop dramatically and hence the reliability of compression goes up—at the cost of more bits. Here, it should be appreciated that, although compression is included in an embodiment of the invention so as to mitigate the growth in bits of output, it is expected that in many cases (such as MPEG streams) it will be ignored all together.

One of ordinary skill would readily appreciate the advantages of the process provided by the present invention. For example, if one chose to encrypt the output of the process, it would only be necessary to encrypt H−W+1 streams of output since the remaining W−1 streams could not be recovered directly without solving the encryption. Also, a net result of the process is that one can recreate a damaged ECC header, wherein a much greater recovery than otherwise possible is achieved since the strength of the error correction scheme is multiplied.

A brief explanation regarding the underlying mathematics of the invention is now provided. First, we consider having a vector A of input values W long, wherein we can create a family of H linear equations in W variables such that any subset of W equations is sufficient to recover the original input vector. In vector notation, this equation can be written as

A*D=E

Where A is a generator matrix of width W and height H which generates the equations, D is the data vector and E is the result vector.

The generator matrix, however, must have some special properties. For example, the generator matrix must never be singular (i.e., it must always be solvable), even when an arbitrary collection of rows is removed (i.e., as long as there are at least W rows remaining). Here, it should be noted that the matrix used is a Vandermonde matrix derivative which is defined as follows:

$V = \begin{bmatrix} 1 & 2 & 3 & \ldots \\ 1^{2} & 2^{2} & 3^{2} & \ldots \\ 1^{3} & 2^{3} & 3^{3} & \ldots \\ \vdots & \vdots & \vdots & \ddots \\ \; & \; & \; & \; \end{bmatrix}$ V_(x, y) = x^(y)

or

The array continues without end to the right and downward. This matrix has the unique property that it always produces linearly independent (orthogonal) equations. In other words, regardless of how the matrix is subset, it may always be inverted. In an embodiment of the invention, the matrix is subset by leaving off the first row (which is trivial but not required) and then selecting H rows which are W wide.

To work a simple example, assume the input is D=[3,1,4,1] and H=5, which would result in the following situation:

$\quad\begin{matrix} {{\begin{bmatrix} 1 & 2 & 3 & 4 \\ 1 & 4 & 9 & 16 \\ 1 & 8 & 27 & 64 \\ 1 & 16 & 81 & 256 \\ 1 & 32 & 243 & 1024 \end{bmatrix} \times \begin{bmatrix} 3 \\ 1 \\ 4 \\ 1 \end{bmatrix}} = \begin{bmatrix} E_{0} \\ E_{1} \\ E_{2} \\ E_{3} \\ E_{4} \end{bmatrix}} \\ {= \begin{bmatrix} {{1 \star 3} + {2 \star 1} + {3 \star 4} + {4 \star 1}} \\ {{1 \star 3} + {4 \star 1} + {9 \star 4} + {16 \star 1}} \\ {{1 \star 3} + {8 \star 1} + {27 \star 4} + {64 \star 1}} \\ {{1 \star 3} + {16 \star 1} + {81 \star 4} + {256 \star 1}} \\ {{1 \star 3} + {32 \star 1} + {243 \star 4} + {1024 \star 1}} \end{bmatrix}} \\ {= \begin{bmatrix} 21 \\ 59 \\ 183 \\ 599 \\ 2031 \end{bmatrix}} \end{matrix}$

So, the ‘redundant values’ are E=[21,59,183,599,2031]. Using any four of these values, the original data in the D vector may be recovered.

Now assume that one wishes to recover using only the first four rows. In this case, the recovery matrix may be calculated by creating a four-by-four matrix using the top four rows of the generator matrix and the corresponding four entries of the E matrix. (Note: If the 3^(rd) row was to be omitted instead, then the third E entry would have been deleted.) Inverting the matrix would then solve the simultaneous equations, wherein it is again noted that any subset of the generator matrix may always be inverted.

$\begin{bmatrix} 1 & 2 & 3 & 4 \\ 1 & 4 & 9 & 16 \\ 1 & 8 & 27 & 64 \\ 1 & 16 & 81 & 256 \end{bmatrix}^{- 1} = \begin{bmatrix} 4 & {- 4.3333} & 1.5 & {- {.1667}} \\ {- 3} & 4.75 & {- 2} & {.25} \\ 1.3333 & {- 2.3333} & 1.1667 & {- {.1667}} \\ {- {.25}} & 0.4583 & {- {.25}} & {.04167} \end{bmatrix}$

Next, we simply multiply the recovery matrix by the E matrix (with the missing row) to realize the original data, wherein rounding off to nearest integer may be needed:

${\begin{bmatrix} 4 & {- 4.3333} & 1.5 & {- {.1667}} \\ {- 3} & 4.75 & {- 2} & {.25} \\ 1.3333 & {- 2.3333} & 1.1667 & {- {.1667}} \\ {- {.25}} & 0.4583 & {- {.25}} & {.04167} \end{bmatrix} \times \begin{bmatrix} 21 \\ 59 \\ 183 \\ 599 \end{bmatrix}} = \begin{bmatrix} 3 \\ 1 \\ 4 \\ 1 \end{bmatrix}$

It should be noted that there are several drawbacks with the approach just described. Round-off errors, for example, might be prevalent, which may result in a recovery of data that is different than the initial data. Also, because the number of bits required to store the E array entries isn't fixed, it could take many more bits to store E_(i) than to store D_(i). Therefore, in order to efficiently and effectively bound the number of bits required, it is generally desirable to express E_(i) in the same number of bits as D_(i).

Another approach, however, is to substitute traditional arithmetic with finite field (Galois) arithmetic using base 4, 8, 16, 32, etc. Several aspects of implementing such finite field arithmetic must first be noted. For example, assuming base 8, there are at most 2⁸ or 256 values for base 8, which means the symbols represent 0-255. As such, all operations must map back within this range (by definition). In other words, no matter what sequence of operations are performed, a value in the range 0-255 must be returned. As a result, “wrap around” effects may yield unwanted complications. Further, addition (‘+’) is implemented by the XOR operation on the binary representation of the number. This means that 4+1=5 but 4+4=0. Also 4−1=1−4=5. Subtraction (‘−’) is identical to Addition. Multiplication is implemented as the discrete antilog of the sum of the logs of the arguments. Similarly, division is the discrete antilog of the difference of the discrete logs of the arguments. Generation of the discrete logarithm tables is simple given the bit pattern for the generating polynomial.

The Vandermonde generator matrix now begins to look a bit different.

$\quad\begin{bmatrix} 1 & 2 & 3 & 4 & \ldots \\ 1 & 4 & 5 & 3 & \ldots \\ 1 & 8 & 15 & 12 & \ldots \\ 1 & 3 & 2 & 5 & \ldots \\ \vdots & \vdots & \vdots & \vdots & \ddots \end{bmatrix}$

Since multiplication is ‘closed’ in this type of arithmetic, there is no way to overflow. Further, the results are precise, without round off error, and they take up exactly the same number of bits (in this case of base 8, it takes up 8 bits).

The code required to multiply and divide requires significant processing such that it is worthwhile to build up multiplication and division tables. In base 8, the tables are really arrays of 256×256 or 64K bytes. For base 16, the tables would be 8 gigabytes long each which is currently impractical. However, since we will seldom need the entire table, a preferred method is to generate C*X tables where C is a constant. These tables would be 2 bytes per entry for base 16 so they would be 128 KB each. For the above Vandermonde matrix subset, there are 8 discrete constants (1,2,3,4,5,8,12,15) so the multiplication tables would require 1 megabyte. The x1 table can be omitted for further savings. Division is used sufficiently seldom that the division table could be eliminated and instead use the example C language routine while having little real performance impact.

Since the contents of the Vandermonde matrix are known in advance, such components make it possible to speed up the process dramatically.

Systems, methods, and components may also implement features akin to encryption keys by specifying which rows of the Vandermonde matrix are used to generate which output streams. There is no requirement that the rows be sequential or in any order. This provides, inter alia, low level encryption/encryption features at zero additional compute overhead.

Innovations consistent with products using this technology include efficient performance of the matrix multiplication to generate the redundant data. A matrix will seldom have to be inverted since one will seldom be loosing different data sources. While it is possible to hard code the matrix multiply on the generation side, it is more difficult on the recovery side since the values are not known in advance.

Turning back to another exemplary hardware implementation, FIG. 12 is block diagram illustrating hardware/logic components consistent with certain aspects related to the innovations herein. Environment 1200 of FIG. 12 shows transmission of streams of bytes 1204 to a computer processing component or device 1202 containing RAM and computational elements capable of implementing the innovations herein. Processing device 1202 may include RAM components 1212, 1218, 1220 loaded with the pre-computed matrix information of width 1214 specified by the matrix and base operating system, as explained above. In this example, the streams 1202 correspond to columns of the data, and each stream is mapped to the appropriate tables or RAM segments containing the values for the matrix in question. After the matrix multiply operations are performed, the intermediary values and passed to an XOR grid 1222 to perform a summation procedure that produces the final output data or streams of output data 1224. FIG. 12 may be the basis for either a dedicated hardware, FPG or software approach. Processors with wide data paths can calculate multiple H channels in parallel. 128 bit wide SSE-style registers or simply 64 bit accumulators can be used to calculate 16 or 8 channels respectively. This takes advantage of the fact that the encodings can be known a priori.

In the present description, the terms component, module, and functional unit, may refer to any type of logical or functional process or blocks that may be implemented in a variety of ways. For example, the functions of various blocks can be combined with one another into any other number of modules. Each module can be implemented as a software program stored on a tangible memory (e.g., random access memory, read only memory, CD-ROM memory, hard disk drive) to be read by a central processing unit to implement the functions of the innovations herein. Or, the modules can comprise programming instructions transmitted to a general purpose computer or to graphics processing hardware via a transmission carrier wave. Also, the modules can be implemented as hardware logic circuitry implementing the functions encompassed by the innovations herein. Finally, the modules can be implemented using special purpose instructions (SIMD instructions), field programmable logic arrays or any mix thereof which provides the desired level performance and cost.

As disclosed herein, embodiments and features of the invention may be implemented through computer-hardware, software and/or firmware. For example, the systems and methods disclosed herein may be embodied in various forms including, for example, a data processor, such as a computer that also includes a database, digital electronic circuitry, firmware, software, or in combinations of them. Further, while some of the disclosed implementations describe components such as software, systems and methods consistent with the innovations herein may be implemented with any combination of hardware, software and/or firmware. Moreover, the above-noted features and other aspects and principles of the innovations herein may be implemented in various environments. Such environments and related applications may be specially constructed for performing the various processes and operations according to the invention or they may include a general-purpose computer or computing platform selectively activated or reconfigured by code to provide the necessary functionality. The processes disclosed herein are not inherently related to any particular computer, network, architecture, environment, or other apparatus, and may be implemented by a suitable combination of hardware, software, and/or firmware. For example, various general-purpose machines may be used with programs written in accordance with teachings of the invention, or it may be more convenient to construct a specialized apparatus or system to perform the required methods and techniques.

Aspects of the method and system described herein may be implemented as functionality programmed into any of a variety of circuitry, including programmable logic devices (“PLDs”), such as field programmable gate arrays (“FPGAs”), programmable array logic (“PAL”) devices, electrically programmable logic and memory devices and standard cell-based devices, as well as application specific integrated circuits. Some other possibilities for implementing aspects include: memory devices, microcontrollers with memory (such as EEPROM), embedded microprocessors, firmware, software, etc. Furthermore, aspects may be embodied in microprocessors having software-based circuit emulation, discrete logic (sequential and combinatorial), custom devices, fuzzy (neural) logic, quantum devices, and hybrids of any of the above device types. The underlying device technologies may be provided in a variety of component types, e.g., metal-oxide semiconductor field-effect transistor (“MOSFET”) technologies like complementary metal-oxide semiconductor (“CMOS”), bipolar technologies like emitter-coupled logic (“ECL”), polymer technologies (e.g., silicon-conjugated polymer and metal-conjugated polymer-metal structures), mixed analog and digital, and so on.

It should also be noted that the various functions disclosed herein may be described using any number of combinations of hardware, firmware, and/or as data and/or instructions embodied in various machine-readable or computer-readable media, in terms of their behavioral, register transfer, logic component, and/or other characteristics. Computer-readable media in which such formatted data and/or instructions may be embodied include, but are not limited to, non-volatile storage media in various forms (e.g., optical, magnetic or semiconductor storage media) and carrier waves that may be used to transfer such formatted data and/or instructions through wireless, optical, or wired signaling media or any combination thereof. Examples of transfers of such formatted data and/or instructions by carrier waves include, but are not limited to, transfers (uploads, downloads, e-mail, etc.) over the Internet and/or other computer networks via one or more data transfer protocols (e.g., HTTP, FTP, SMTP, and so on).

Unless the context clearly requires otherwise, throughout the description and the claims, the words “comprise,” “comprising,” and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is to say, in a sense of “including, but not limited to.”. Words using the singular or plural number also include the plural or singular number respectively. Additionally, the words “herein,” “hereunder,” “above,” “below,” and words of similar import refer to this application as a whole and not to any particular portions of this application. When the word “or” is used in reference to a list of two or more items, that word covers all of the following interpretations of the word: any of the items in the list, all of the items in the list and any combination of the items in the list.

Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the disclosure above in combination with the following paragraphs describing the scope of one or more embodiments of the following invention. 

1. A method of processing data comprising: producing W data inputs from input data; generating H discrete output data streams from the W data inputs via application of the W data inputs to one or more transforming components that perform a transformation process using Galois field operations and a generator matrix characterized as being invertible in all subsets encounterable during recovery of the input data, wherein H≧W and the transformation process includes: for each of the W inputs, producing an H-sized intermediary; combining all H-sized intermediaries into one H-sized result; and processing the one H-sized result into the H discrete output data streams; wherein the W data inputs are recoverable via a recovery process capable of producing the W data inputs from any W members of the H output data streams. 2.-3. (canceled)
 4. The method of claim 1 further comprising applying a check value to one or more of the H output data streams and/or the W input data streams or any prior data stream.
 5. The method of claim 1 further comprising applying a check value to one or more of the W input data streams or any prior data stream.
 6. The method of claim 1 further comprising applying a check value to one or more of any data streams prior to the transformation process.
 7. The method of claim 1 further comprising reading the input in groups of W and supplying the transformation process with W discrete inputs.
 8. The method of claim 4 further comprising storing the check value to allow comparison of the hashes from before and after processing, movement and/or storage to verify the correctness of the result.
 9. The method of claim 4 further comprising storing the check value to allow quick isolation of errors at various processing stages.
 10. The method of claim 4 further comprising storing the check value to enable alternate recovery stream subsets (or chapter subsets) to be used for a region of a stream to recover the proper data.
 11. The method of claim 1 further comprising dividing the H output data streams into chapters so as to produce sets of corresponding chapters of each stream such that a desired chapter of a set may be recovered from corresponding members/chapters of the set.
 12. The method of claim 6 wherein the H output data streams are divided into chapters such that a damaged chapter may be quickly isolated allowing it to be recovered or to be bypassed in the general recovery process without bypassing the entire stream.
 13. The method of claim 1 further comprising dividing the H output data streams into chapters so as to produce sets of corresponding chapters of each stream such that a damaged chapter may be isolated such that it may be one or both of recovered or bypassed in the general recovery process without bypassing the entire stream.
 14. The method of claim 1 further comprising dividing the H output data streams into chapters so as to produce sets of corresponding chapters of each stream such that a damaged chapter may be isolated such that it may be recovered without recovery of any plain text.
 15. The method of claim 1, wherein plain text of transformed data (the H output data streams) cannot be obtained without knowledge of the generator matrix equations plus at least W output data streams.
 16. The method of claim 1, wherein the recovery process includes application of a recovery matrix, and wherein the generator matrix and the recovery matrix are creatable from or based on the mathematical inverse of each other.
 17. The method of claim 1, wherein the one or more transforming components use Galois or finite field arithmetic vis-à-vis the generator matrix. 18.-42. (canceled)
 43. A method of processing data comprising: producing W data inputs from input data; generating H discrete output data streams from the W data inputs via application of the W data inputs to one or more transforming components that perform a transformation process that applies a generator matrix characterized as being invertible in all subsets encounterable during recovery of the input data, wherein H≧W and the transformation process includes: for each of the W inputs, producing an H-sized intermediary; combining all H-sized intermediaries into one H-sized result; and processing the one H-sized result into the H discrete output data streams; thereby producing transformed output data characterized in that: none of the H output data streams corresponds to any W data input plain text; and any subset of the H output data streams with cardinality less than or equal to H−W is recoverable from all remaining members of the H output data streams without knowledge/recovery of plain text of the input data; wherein the W data inputs are recoverable via a recovery process capable of producing the W data inputs from any W members of the H output data streams.
 44. (canceled)
 45. The method of claim 43 further comprising applying a check value to one or more of the H output data streams and/or the W input data streams or any prior data stream.
 46. The method of claim 43 further comprising applying a check value to one or more of the W input data streams or any prior data stream.
 47. The method of claim 43 further comprising applying a check value to one or more of any data streams prior to the transformation process.
 48. The method of claim 43 further comprising reading the input in groups of W and supplying the transformation process with W discrete inputs.
 49. The method of claim 45 further comprising storing the check value to allow comparison of the hashes from before and after processing, movement and/or storage to verify the correctness of the result.
 50. The method of claim 45 further comprising storing the check value to allow quick isolation of errors at various processing stages.
 51. The method of claim 45 further comprising storing the check value to enable alternate recovery stream subsets (or chapter subsets) to be used for a region of a stream to recover the proper data.
 52. The method of claim 43 further comprising dividing the H output data streams into chapters so as to produce sets of corresponding chapters of each stream such that a desired chapter of a set may be recovered from corresponding members/chapters of the set.
 53. The method of claim 47 wherein the H output data streams are divided into chapters such that a damaged chapter may be quickly isolated allowing it to be recovered or to be bypassed in the general recovery process without bypassing the entire stream.
 54. The method of claim 43 further comprising dividing the H output data streams into chapters so as to produce sets of corresponding chapters of each stream such that a damaged chapter may be isolated such that it may be one or both of recovered or bypassed in the general recovery process without bypassing the entire stream.
 55. The method of claim 43 further comprising dividing the H output data streams into chapters so as to produce sets of corresponding chapters of each stream such that a damaged chapter may be isolated such that it may be recovered without recovery of any plain text.
 56. The method of claim 43, wherein plain text of transformed data (the H output data streams) cannot be obtained without knowledge of the generator matrix equations plus at least W output data streams.
 57. The method of claim 43, wherein the recovery process includes application of a recovery matrix, and wherein the generator matrix and the recovery matrix are creatable from or based on the mathematical inverse of each other.
 58. The method of claim 43, wherein the one or more transforming components use Galois or finite field arithmetic vis-à-vis the generator matrix. 59.-84. (canceled)
 85. A method of processing data comprising: producing W data inputs from input data; generating H output data streams from the W data inputs via application of the W data inputs to one or more transforming components that perform a transformation process that applies a generator matrix characterized as being invertable in all subsets encounterable during recovery of the input data, wherein H>W and the transformation process includes: for each of the W inputs, producing an H-sized intermediary; combining all H-sized intermediaries into one H-sized result; and processing the one H-sized result into the H output data streams, wherein none of the H output data streams corresponds to any W data input plain text; and storing the H output streams in disparate data stores, wherein no single data store contains output streams sufficient to recover the input data, such that the destruction of or loss of connectivity to or otherwise unavailability any quantity of the data stores from 1 through H−W does not impede the ability to recover the stored data; wherein the W data inputs are recoverable via a recovery process capable of producing the W data inputs from any W members of the H output data streams.
 86. The method of claim 85 further comprising regenerating data stored in any of the disparate data stores via at least W of the H output data streams without revelation of the plain text.
 87. The method of claim 85, wherein each of the H output streams is stored in a separate data store such that access to W separate data stores is required to recover the input data.
 88. The method of claim 85, wherein the disparate data stores are located in different jurisdictions.
 89. The method of claim 85 wherein the disparate data stores are located in multiple jurisdictions.
 90. The method of claim 85, wherein the disparate data stores are located in different countries.
 91. The method of claim 85 wherein the disparate data stores are located in multiple countries.
 92. The method of claim 88 wherein the disparate stores are located in a collection of different and/or multiple jurisdictions, different and/or multiple countries and areas without established or clearly agreed upon jurisdiction or governmental control.
 93. The method of claim 88 wherein the data stored is backup, historical or archival data.
 94. The method of claim 85, further comprising transforming and storing data covered by a heightened duty or privilege such as medical information, accounting information, attorney-client privileged information, email, personnel files, content declared classified, thereby requiring storage which is both safer from disaster than normal and also more difficult to be disclosed to third parties. 95.-100. (canceled)
 101. A method of processing data comprising: receiving a first quantity (W) of data inputs produced from input data; generating a second quantity (H) of discrete output data streams from the W data inputs via application of the W data inputs to one or more transforming components that perform a transformation process using Galois field operations and a generator matrix characterized as being invertible in all subsets encounterable during recovery of the input data, wherein: H≧W; the H discrete data output streams are generated in systematic format to each include portions of the input data; and the transformation process includes: for each of the W inputs, producing an H-sized intermediary; combining all H-sized intermediaries into one H-sized result; and processing the one H-sized result into the H discrete output data streams; storing the H discrete output data streams in H redundant arrays of independent storage devices such that loss of H−W of the devices does not result in a loss of data, wherein the input data cannot be recovered from information existing in fewer than W devices.
 102. A method of processing data comprising: receiving a first quantity (W) of data inputs produced from input data; generating a second quantity (H) of discrete output data streams from the W data inputs via application of the W data inputs to one or more transforming components that perform a transformation process using Galois field operations and a generator matrix characterized as being invertible in all subsets encounterable during recovery of the input data, wherein: H≧W; the H discrete data output streams are regions or sectors generated in systematic format from the W data inputs; and the transformation process includes: for each of the W inputs, producing an H-sized intermediary; combining all H-sized intermediaries into one H-sized result; and processing the one H-sized result into the H discrete output data streams; and storing the H outputs within one or more storage devices such that the loss of one or more regions or sectors can be recovered from the remaining information.
 103. A system that processes data, the system comprising: a processing component that produces a first quantity (W) of data inputs from input data; one or more transforming components that perform a transformation process on the W data inputs using Galois field operations and a generator matrix characterized as being invertible in all subsets encounterable during recovery of the input data; a generating component that generates a second quantity (H) of output data streams from the W data inputs; wherein H is greater than, or greater than or equal to, W and the transformation process includes: for each of the W inputs, producing an H-sized intermediary; combining all H-sized intermediaries into one H-sized result; and processing the one H-sized result into the H output data streams, wherein the W data inputs are recoverable via a recovery process capable of reproducing the W data inputs from any subset of the H output data streams with cardinality W. 